Types of geographic data

There are three main types of geographic data:

• points
• lines
• polygons

People often also talk about images as geographic data, because so many maps come that way (tiles from googlemaps and OpenStreetMap, satellite imagery). So, we could also think about the difference between

• vector data (lines and polygons), and
• raster data (images)

Tiles

Mapbox has a nice explanation of how map tiles work. Let’s check it out!

Leaflet

Leaflet is a JavaScript mapping library.

This means it plays nicer with things like d3, but we can use it in R as well, through an htmlwidget.

Points

Making a map with points in R with leaflet

We’re going to be doing a lot of the stuff from the RStudio leaflet tutorial.

library(leaflet)

m <- leaflet() %>%
  setView(-93.2650, 44.9778, zoom = 4) %>%
  addProviderTiles("Stamen.Toner") %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-93.2650, lat=44.9778, popup="Minneapolis!")
m

We need more data!!

Lets look at storm data from the NOAA. It comes in a few files that we need to join together in order to use.

library(readr)
library(dplyr)

library(RCurl)

stormlocs <- getURL("https://raw.githubusercontent.com/AmeliaMN/SummerDataViz/master/Geo/StormEvents_locations-ftp_v1.0_d2016_c20160810.csv")
stormlocs <- read_csv(stormlocs)
stormdetails <- getURL("https://raw.githubusercontent.com/AmeliaMN/SummerDataViz/master/Geo/StormEvents_details-ftp_v1.0_d2016_c20160810.csv")
stormdetails <- read_csv(stormdetails)


#stormlocs <- read_csv("StormEvents_locations-ftp_v1.0_d2016_c20160810.csv")
#stormdetails <- read_csv("StormEvents_details-ftp_v1.0_d2016_c20160810.csv")
stormlocs <- stormlocs %>%
  left_join(stormdetails, by="EVENT_ID")
lightning <- stormlocs %>%
  filter(EVENT_TYPE=="Lightning")

Mapping the lightning

Now, we can programmatically map them.

m <- leaflet(data=lightning) %>%
  addMarkers(~LONGITUDE, ~LATITUDE)

# OpenStreetMap
m %>%
  addTiles()

# Stamen
m %>% 
  addProviderTiles("Stamen.Toner") 

Find another storm type to map

Bonus– add popups!

One approach

mtw <- stormlocs %>%
  filter(EVENT_TYPE=="Marine Thunderstorm Wind")

m <- leaflet(data=mtw) %>%
  addProviderTiles("Stamen.Toner") %>% 
  addMarkers(~LONGITUDE, ~LATITUDE, popup=~EVENT_NARRATIVE)

m

Plotting lots of points

I love the Racial Dot Map. Let’s go check it out!

Lines

I don’t have much to say here

• Streets?
• ??

Polygons

Lets start with something easy(ish) – circles

tornados <- stormlocs %>%
  filter(EVENT_TYPE=="Tornado")

m <- leaflet(data=tornados) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addCircles(~LONGITUDE, ~LATITUDE, weight = 1, radius = ~DAMAGE_PROPERTY*100, popup = ~EVENT_NARRATIVE)
m

But usually, you want boundaries

Most of the time, you need to aggregate your data in some way. Think about states, countries, zipcodes, counties, etc. Data often comes pre-aggregated at a particular spatial aggregation, or you can’t show all the individual points.

Old-school choropleth map

More modern choropleth

The best and worst places to grow up: How your area compares

Polgyons come in shapefiles

Most boundaries (state, national, etc) are provided in terms of polygons. Major mapping software ArcGIS, from ESRI, has essentially set the standard formats.

Look in the GitHub repo to see some examples of how this looks. There are many files with different extensions: .prj (the projection), .shp (the shapefile), .cpg (??), .dbf (??), .shx (??).

You need special software or packages to work with shapefiles.

State shapefiles

I got these from the Census. You can choose the resolution.

If you want, the zipfile of the shapes I used is here.

We’re going to use the rgdal package to deal with shapefiles

library(rgdal)

# In the Console, I need to do a different path

states <- readOGR("cb_2015_us_state_500k", layer = "cb_2015_us_state_500k", verbose = FALSE)

In RStudio, you can click on the states object to see what a Large SpatialPolygonsDataFrame looks like. @data and @polygons.

Boring, internal data from shapefile

leaflet(data=states) %>%
  addTiles() %>%
  addPolygons(stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5, color =~colorQuantile("YlOrRd", states$AWATER)(AWATER)
  )

Something more interesting – need to count up some data

tornadocount <- tornados %>%
  group_by(STATE) %>%
  summarize(n=n())

Colors are tricky!

colorbrewer

colors <- c("#edf8fb", "#b2e2e2", "#66c2a4", "#238b45")
tornadocount <- tornadocount %>%
  mutate(color = cut(n, breaks=quantile(n)))

# Baaaaad factor practice. Do as I say, not as I do?!
levels(tornadocount$color) <- colors

Joining data

states@data$NAME <- toupper(states@data$NAME)
states@data <- left_join(states@data, tornadocount, by=c("NAME"="STATE"))

Putting it all together

leaflet(data=states) %>%
  addTiles() %>%
  addPolygons(stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5, color = ~states@data$color.y)

Some issues with polygon maps

Modifiable areal unit problem

An introduction to the Modifiable Areal Unit Problem

Gerrymandering

The best explanation of gerrymandering you will ever see

Spatial aggregation

My colleague Aran Lunzer and I made a demo of how spatial aggregation can impact the visual pattern you see. Let’s go check it out!